As machine learning is increasingly deployed in high-stakes contexts affecting people's
livelihoods, there have been growing calls to “open the black box” and to make machine …

As machine learning is increasingly deployed in high-stakes contexts affecting people's
livelihoods, there have been growing calls to open the black box and to make machine …

Explainable machine learning offers the potential to provide stakeholders with insights into
model behavior by using various methods such as feature importance scores, counterfactual …

Explainability is highly desired in machine learning (ML) systems supporting high-stakes
policy decisions in areas such as health, criminal justice, education, and employment. While …

The notion that algorithmic systems should be" explainable" is common in the many
statements of consensus principles developed by governments, companies, and advocacy …

Machine learning models in safety-critical settings like healthcare are often “blackboxes”:
they contain a large number of parameters which are not transparent to users. Post-hoc …

Artificial intelligence (AI) provides many opportunities to improve private and public life.
Discovering patterns and structures in large troves of data in an automated manner is a core …

Machine learning in decision support systems already outperforms pre-existing statistical
methods. However, their predictions face challenges as calculations are often complex and …

Learning methods such as boosting and deep learning have made ML models harder to
understand and interpret. This puts data scientists and ML developers in the position of often …

The application of complex machine learning models has elicited research to make them
more explainable. However, most explainability methods cannot provide insight beyond the …